Occurrence, fate, and confounding influence of ghost passive integrated transponder tags in an intensively monitored watershed
Bibliographic record
Abstract
Over the last three decades, passive integrated transponder (PIT) tags have been widely used to study fish populations. Interpretation of PIT tag detections, however, can be confounded by the presence of ghost tags, tags liberated when a fish dies. We used a combination of mobile antenna surveys, stationary antenna detections, and multistate mark–recapture modeling to assess the abundance and fate of ghost tags in a coastal California watershed. Accumulation of ghost tags from released hatchery-origin coho salmon (Oncorhynchus kisutch) smolts was substantial during California’s recent drought, with 2224 ghost tags identified during mobile reader surveys. Between surveys, PIT tags moved downstream a median distance of 346 m and a maximum distance of 1982 m. Stationary antenna array detections indicated that these movements occurred during high-flow events, concurrent with live fish movement. The multistate model estimated that, during winter, approximately 40% of tags were buried in the substrate beyond the read range of mobile readers. Failure to account for transport and burial dynamics of ghost tags can lead to biased estimates of fish abundance, survival, and movement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".